update dataset card

README.md

@@ -10,58 +10,40 @@ configs:
         path: flickr30k/test-*
 ---
 
-# LARE: Dense-Set
+# Dense-Set
 
-**Dense-Set** is a curated benchmark of visually dense scenes tailored for text-to-image retrieval. It provides challenging evaluation subsets extracted from MS-COCO and Flickr30K, containing crowded images with multiple object instances and underrepresented, low-attention classes.
+**Dense-Set** is a curated benchmark of visually dense scenes for text-to-image retrieval evaluation. It provides challenging subsets extracted from COCO and Flickr30K, focusing on crowded images with multiple object instances and underrepresented, low-attention classes.
 
-This dataset is the official benchmark published alongside our paper:
+This dataset is published alongside:
 
-> **LARE: Low-Attention Region Encoding for Text–Image Retrieval**<br>
-> *Accepted at the [CVPR 2026 MULA Workshop](https://mula-workshop.github.io/)*
+> **LARE: Low-Attention Region Encoding for Text–Image Retrieval**
+> Abdulmalik Alquwayfili, Faisal Almeshal, Jumanah Almajnouni, Leena Alotaibi, Huda Alamri, Muhammad Kamran J Khan
+> *CVPR 2026 — [MULA Workshop](https://mula-workshop.github.io/)*
+> [Project Page](https://falmeshal.github.io/LARE/) | [Code](https://github.com/AbdulmalikDS/LARE)
 
-## Dataset Construction & Re-captioning
+## Construction
 
-Dense-Set was constructed through an automated pipeline carefully designed to emphasize objects traditional vision-language models overlook:
-1. **High-Density Filtering**: Images were processed using a YOLO object detector. We ranked images by total object count and isolated the top 10% to create a high-density candidate pool.
-2. **Rare-Class Isolation**: Within the dense pool, we identified "rare classes" at the image level—defined as object categories appearing exactly once in a given image. 
-3. **BLIP-2 Re-captioning**: To shift the textual focus away from general scene context, we filtered out rare-class detections occupying >15% of the image area. We then prompted BLIP-2 with class-aware templates to explicitly describe these small or underrepresented objects, producing highly challenging, fine-grained captions.
+Dense-Set was built through a three-stage pipeline designed to surface objects that standard vision-language models overlook:
 
-## Dataset Statistics
+1. **High-Density Filtering** — Images processed with YOLO, ranked by total object count, top 10% retained as the high-density candidate pool.
+2. **Rare-Class Isolation** — Within the dense pool, object categories appearing exactly once per image are flagged as rare classes, corresponding to small or visually subordinate objects.
+3. **Re-captioning** — Rare-class detections occupying >15% of the image are filtered out. BLIP-2 is prompted with class-aware templates to explicitly describe the remaining underrepresented objects, producing fine-grained captions that shift focus away from dominant scene context.
+
+## Statistics
 
 | Dataset | Split | # Images | Avg. Objects | Avg. # Classes |
-| :--- | :--- | :--- | :--- | :--- |
-| **MS-COCO** | *Original Test Set* | 40,504 | 6.71 | 2.85 |
-| | *High-Density Subset* | 4,050 | 21.63 | 4.82 |
+| :--- | :--- | ---: | ---: | ---: |
+| **COCO** | Original Test Set | 40,504 | 6.71 | 2.85 |
+| | High-Density Subset | 4,050 | 21.63 | 4.82 |
 | | **Dense-Set** | **3,089** | **21.63** | **5.47** |
-| **Flickr30K** | *Original Test Set* | 31,783 | 6.73 | 2.48 |
-| | *High-Density Subset* | 3,178 | 19.40 | 4.38 |
+| **Flickr30K** | Original Test Set | 31,783 | 6.73 | 2.48 |
+| | High-Density Subset | 3,178 | 19.40 | 4.38 |
 | | **Dense-Set** | **2,477** | **19.55** | **4.85** |
 
-## Loading the Data
-
-You can explore the two individual subsets cleanly separated using the dropdown menu at the top of the Hugging Face Dataset Viewer. All subset images are natively hosted on this repository within a Parquet structure.
-
-```python
-from datasets import load_dataset
-
-# Load COCO Dense-Set
-coco_ds = load_dataset("AbdulmalekDS/Dense-Set", "coco")
+## Usage
 
-# Load Flickr30k Dense-Set
-flickr_ds = load_dataset("AbdulmalekDS/Dense-Set", "flickr30k")
 
-print(coco_ds["test"][0])
-```
 
 ## Citation
 
-*Please use the following citation to reference the LARE Dense-Set benchmark:*
 
-```bibtex
-@inproceedings{alquwayfili2026lare,
-  title={LARE: Low-Attention Region Encoding for Text--Image Retrieval},
-  author={Abdulmalik Alquwayfili and Faisal Almeshal and Jumanah Almajnouni and Leena Alotaibi and Huda Alamri and Muhammad Kamran J Khan},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
-  year={2026}
-}
-```